Detectable MeV neutrinos from black hole neutrino-dominated accretion flows

Neutrino-dominated accretion flows (NDAFs) around rotating stellar-mass black holes (BHs) have been theorized as the central engine of relativistic jets launched in massive star core collapse events or compact star mergers. In this work, we calculate the electron neutrino/anti-neutrino spectra of NDAFs by fully taking into account the general relativistic effects, and investigate the effects of viewing angle, BH spin, and mass accretion rate on the results. We show that even though a typical NDAF has a neutrino luminosity lower than that of a typical supernova (SN), it can reach $10^{50}-10^{51}~{\rm erg~s^{-1}}$ peaking at $\sim 10$ MeV, making them potentially detectable with the upcoming sensitive MeV neutrino detectors if they are close enough to Earth. Based on the observed GRB event rate in the local universe and requiring that at least 3 neutrinos are detected to claim a detection, we estimate a detection rate up to $\sim$ (0.10-0.25) per century for GRB-related NDAFs by the Hyper-Kamiokande (Hyper-K) detector if one neglects neutrino oscillation. If one assumes that all Type Ib/c SNe have an engine-driven NDAF, the Hyper-K detection rate would be $\sim$ (1-3) per century. By considering neutrino oscillations, the detection rate may decrease by a factor of 2-3. Detecting one such event would establish the observational evidence of NDAFs in the universe.Comment: 7 pages, 2 figures, 2 tables, accepted for publication in PR

EMP-SSL: Towards Self-Supervised Learning in One Training Epoch

Recently, self-supervised learning (SSL) has achieved tremendous success in learning image representation. Despite the empirical success, most self-supervised learning methods are rather "inefficient" learners, typically taking hundreds of training epochs to fully converge. In this work, we show that the key towards efficient self-supervised learning is to increase the number of crops from each image instance. Leveraging one of the state-of-the-art SSL method, we introduce a simplistic form of self-supervised learning method called Extreme-Multi-Patch Self-Supervised-Learning (EMP-SSL) that does not rely on many heuristic techniques for SSL such as weight sharing between the branches, feature-wise normalization, output quantization, and stop gradient, etc, and reduces the training epochs by two orders of magnitude. We show that the proposed method is able to converge to 85.1% on CIFAR-10, 58.5% on CIFAR-100, 38.1% on Tiny ImageNet and 58.5% on ImageNet-100 in just one epoch. Furthermore, the proposed method achieves 91.5% on CIFAR-10, 70.1% on CIFAR-100, 51.5% on Tiny ImageNet and 78.9% on ImageNet-100 with linear probing in less than ten training epochs. In addition, we show that EMP-SSL shows significantly better transferability to out-of-domain datasets compared to baseline SSL methods. We will release the code in https://github.com/tsb0601/EMP-SSL

PhysBench: A Benchmark Framework for rPPG with a New Dataset and Baseline

In recent years, due to the widespread use of internet videos, physiological remote sensing has gained more and more attention in the fields of affective computing and telemedicine. Recovering physiological signals from facial videos is a challenging task that involves a series of preprocessing, image algorithms, and post-processing to finally restore waveforms. We propose a complete and efficient end-to-end training and testing framework that provides fair comparisons for different algorithms through unified preprocessing and post-processing. In addition, we introduce a highly synchronized lossless format dataset along with a lightweight algorithm. The dataset contains over 32 hours (3.53M frames) of video from 58 subjects; by training on our collected dataset both our proposed algorithm as well as existing ones can achieve improvements

Transient analysis of arm locking controller

Arm locking is one of the key technologies to suppress the laser phase noise in spaced-based gravitational waves observatories. Since arm locking was proposed, phase margin criterion was always used as the fundamental design strategy for the controller development. In this paper, we find that this empirical method from engineering actually cannot guarantee the arm locking stability. Therefore, most of the advanced arm locking controllers reported so far may have instable problems. After comprehensive analysis of the single arm locking's transient responses, strict analytical stability criterions are summarized for the first time. These criterions are then generalized to dual arm locking, modified-dual arm locking and common arm locking, and special considerations for the design of arm locking controllers in different architectures are also discussed. It is found that PI controllers can easily meet our stability criterions in most of the arm locking systems. Using a simple high gain PI controller, it is possible to suppress the laser phase noise by 5 orders of magnitude within the science band. Our stability criterions can also be used in other feedback systems, where several modules with different delays are connected in parallel.Comment: 24 pages, 24 figure

Alcohol Consumption and Ankle-to-Brachial Index: Results from the Cardiovascular Risk Survey

BACKGROUND AND METHODOLOGY: A low ankle-to-brachial index (ABI) is a strong correlate of cardiovascular disease and subsequent mortality. The relationship between ABI and alcohol consumption remains unclear. Data are from the Cardiovascular Risk Survey (CRS), a multiple-ethnic, community-based, cross-sectional study of 14,618 Chinese people (5757 Hans, 4767 Uygurs, and 4094 Kazakhs) aged 35 years and over at baseline from Oct. 2007 to March 2010. The relationship between alcohol intake and ABI was determined by use of analysis of covariance and multivariable regressions. PRINCIPAL FINDINGS: In men, alcohol consumption was significantly associated with ABI (P<0.001). After adjusted for the confounding factors, such as age, sex, ethnicity, body mass index, smoking, work stress, diabetes, and fasting blood glucose, the difference remained significant (P<0.001); either the unadjusted or multivariate-adjusted odds ratio (OR) for peripheral artery disease (PAD) was significantly higher in men who consumed >60.0 g/d [OR = 3.857, (95% CI: 2.555-5.824); OR = 2.797, (95% CI: 1.106-3.129); OR = 2.878, (95% CI: 1.215-4.018); respectively] and was significantly lower in men who consumed 20.1-40.0 g/d [OR= 0.330, (95% CI: 0.181-0.599); OR = 0.484, (95% CI: 0.065-0.894); OR = 0.478, (95% CI: 0.243-1.534); respectively] and 40.1-60.0 g/d [OR= 0.306, (95% CI: 0.096-0.969); OR = 0.267, (95% CI: 0.087-0.886); OR = 0.203, (95% CI: 0.113-0.754); respectively] compared with never drinking, respectively (all P<0.01). Neither in unadjusted nor in multivariate-adjusted model was the association between ABI and alcohol consumption significant (all P>0.05) in women. Similarly, PAD was not correlated with alcohol intake in women (all P>0.05). CONCLUSIONS/SIGNIFICANCE: Our results indicated that in Chinese men, alcohol consumption was associated with peripheral artery disease, and consumption of less than 60 g/d had an inverse association with peripheral atherosclerosis whereas consumption of 60 g/d or more had a positive association

Unsupervised Manifold Linearizing and Clustering

We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine learning and computer vision. When the manifolds are assumed to be linear subspaces, this reduces to the classical problem of subspace clustering, which has been studied extensively over the past two decades. Unfortunately, many real-world datasets such as natural images can not be well approximated by linear subspaces. On the other hand, numerous works have attempted to learn an appropriate transformation of the data, such that data is mapped from a union of general non-linear manifolds to a union of linear subspaces (with points from the same manifold being mapped to the same subspace). However, many existing works have limitations such as assuming knowledge of the membership of samples to clusters, requiring high sampling density, or being shown theoretically to learn trivial representations. In this paper, we propose to optimize the Maximal Coding Rate Reduction metric with respect to both the data representation and a novel doubly stochastic cluster membership, inspired by state-of-the-art subspace clustering results. We give a parameterization of such a representation and membership, allowing efficient mini-batching and one-shot initialization. Experiments on CIFAR-10, -20, -100, and TinyImageNet-200 datasets show that the proposed method is much more accurate and scalable than state-of-the-art deep clustering methods, and further learns a latent linear representation of the data